Interest has grown recently in the application of computational and statistical tools to problems in the analysis of algorithms. In many algorithmic domains, worst-case bounds are too pessimistic and tractable probabilistic models too unrealistic to provide meaningful predictions of practical algorithmic performance. Experimental approaches can provide knowledge where purely analytical methods fail and can provide insights to motivate and guide deeper analytical results. The DIMACS Implementation Challenge was organized to encourage experimental work in the area of network flows and matchings. Participants at sites in the U.S., Europe, and Japan undertook projects between November 1990 and August 1991 to test and evaluate algorithms for these problems. The Challenge culminated in a three-day workshop, held in October 1991 at DIMACS. This volume contains the revised and refereed versions of twenty-two of the papers presented at the workshop, along with supplemental material about the Challenge and the Workshop.

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A rigorous and comprehensive treatment of network flow theory and monotropic optimization by one of the world's most renowned applied mathematicians. This classic textbook covers extensively the duality theory and the algorithms of linear and nonlinear network optimization optimization, and their significant extensions to monotropic programming (separable convex constrained optimization problems, including linear programs). It complements our other book on the subject of network optimization Network Optimization: Continuous and Discrete Models (Athena Scientific, 1998). Monotropic programming problems are characterized by a rich interplay between combinatorial structure and convexity properties. Rockafellar develops, for the first time, algorithms and a remarkably complete duality theory for these problems. Among its special features the book: (a) Treats in-depth the duality theory for linear and nonlinear network optimization (b) Uses a rigorous step-by-step approach to develop the principal network optimization algorithms (c) Covers the main algorithms for specialized network problems, such as max-flow, feasibility, assignment, and shortest path (d) Develops in detail the theory of monotropic programming, based on the author's highly acclaimed research (e) Contains many examples, illustrations, and exercises (f) Contains much new material not found in any other textbook

Network flow theory has been used across a number of disciplines, including theoretical computer science, operations research, and discrete math, to model not only problems in the transportation of goods and information, but also a wide range of applications from image segmentation problems in computer vision to deciding when a baseball team has been eliminated from contention. This graduate text and reference presents a succinct, unified view of a wide variety of efficient combinatorial algorithms for network flow problems, including many results not found in other books. It covers maximum flows, minimum-cost flows, generalized flows, multicommodity flows, and global minimum cuts and also presents recent work on computing electrical flows along with recent applications of these flows to classical problems in network flow theory.

Perceptive text examines shortest paths, network flows, bipartite and nonbipartite matching, matroids and the greedy algorithm, matroid intersections, and the matroid parity problems. Suitable for courses in combinatorial computing and concrete computational complexity.

There has been an explosive growth in the field of combinatorial algorithms. These algorithms depend not only on results in combinatorics and especially in graph theory, but also on the development of new data structures and new techniques for analyzing algorithms. Four classical problems in network optimization are covered in detail, including a development of the data structures they use and an analysis of their running time. Data Structures and Network Algorithms attempts to provide the reader with both a practical understanding of the algorithms, described to facilitate their easy implementation, and an appreciation of the depth and beauty of the field of graph algorithms.

In the past decade, primal-dual algorithms have emerged as the most important and useful algorithms from the interior-point class. This book presents the major primal-dual algorithms for linear programming in straightforward terms. A thorough description of the theoretical properties of these methods is given, as are a discussion of practical and computational aspects and a summary of current software. This is an excellent, timely, and well-written work. The major primal-dual algorithms covered in this book are path-following algorithms (short- and long-step, predictor-corrector), potential-reduction algorithms, and infeasible-interior-point algorithms. A unified treatment of superlinear convergence, finite termination, and detection of infeasible problems is presented. Issues relevant to practical implementation are also discussed, including sparse linear algebra and a complete specification of Mehrotra's predictor-corrector algorithm. Also treated are extensions of primal-dual algorithms to more general problems such as monotone complementarity, semidefinite programming, and general convex programming problems.

This book brings together recent developments in Alzheimer's disease research with related discoveries in the field of cell biology. The book moves between basic cell biological concepts that form the underpinnings of modern Alzheimer's disease research, and current findings about proteins and cellular processes affected by the disease. Divided into three topics, the book addresses (1) protein trafficking, a problem that has become germane to the study of the amyloid precursor protein; (2) phosphorylation, a problem that underlies studies of the pathological transformation of tau to paired helical filaments; and (3) cell death, a pervasive problem in neurodegeneration.

You know that servers have log files and performance measuring tools and that traditional network devices have LEDs that blink when a port does something. You may have tools that tell you how busy an interface is, but mostly a network device is a black box. Network Flow Analysis opens that black box, demonstrating how to use industry-standard software and your existing hardware to assess, analyze, and debug your network. Unlike packet sniffers that require you to reproduce network problems in order to analyze them, flow analysis lets you turn back time as you analyze your network. You'll learn how to use open source software to build a flow-based network awareness system and how to use network analysis and auditing to address problems and improve network reliability. You'll also learn how to use a flow analysis system; collect flow records; view, filter, and report flows; present flow records graphically; and use flow records to proactively improve your network. Network Flow Analysis will show you how to: –Identify network, server, router, and firewall problems before they become critical –Find defective and misconfigured software –Quickly find virus-spewing machines, even if they’re on a different continent –Determine whether your problem stems from the network or a server –Automatically graph the most useful data And much more. Stop asking your users to reproduce problems. Network Flow Analysis gives you the tools and real-world examples you need to effectively analyze your network flow data. Now you can determine what the network problem is long before your customers report it, and you can make that silly phone stop ringing.

Guided by standard bioscience workflows in high-throughput sequencing analysis, this book for graduate students, researchers, and professionals in bioinformatics and computer science offers a unified presentation of genome-scale algorithms. This new edition covers the use of minimizers and other advanced data structures in pangenomics approaches.